test(ch13): add deterministic smoke tests for Stroop and Fitness 2x2

tests/test_ch13_fitness.py

@@ -0,0 +1,23 @@
+import statsmodels.formula.api as smf
+from scripts import sim_fitness_2x2 as sim
+
+SEED = 2025
+
+def test_fitness_mixed_model_signals():
+    subjects, long_df = sim.simulate(n_per_group=5, seed=SEED)
+
+    # Mixed model with random intercept for id, categorical time/group + covariates
+    md = smf.mixedlm(
+        "strength ~ C(time) * C(group) + age + sex + bmi",
+        long_df,
+        groups=long_df["id"],
+    )
+    res = md.fit(reml=True)
+
+    # main pre->post should be positive and clearly significant
+    assert res.params["C(time)[T.post]"] > 5
+    assert res.pvalues["C(time)[T.post]"] < 1e-6
+
+    # interaction should be positive (ProgramB a bit more improvement); allow modest p
+    assert res.params["C(time)[T.post]:C(group)[T.ProgramB]"] > 0
+    assert res.pvalues["C(time)[T.post]:C(group)[T.ProgramB]"] < 0.2

tests/test_ch13_stroop.py

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+import numpy as np
+import pandas as pd
+from scipy import stats
+import statsmodels.formula.api as smf
+
+from scripts import sim_stroop
+
+SEED = 2025
+
+def test_stroop_effect_and_mixed_model():
+    subjects, trials = sim_stroop.simulate(n_subjects=6, n_trials=10, seed=SEED)
+
+    # subject-level means by condition (ms)
+    means = (
+        trials[trials["correct"]]
+        .query("rt_ms.between(200, 2000)")
+        .groupby(["subject", "condition"], as_index=False)["rt_ms"]
+        .mean()
+        .pivot(index="subject", columns="condition", values="rt_ms")
+        .dropna()
+    )
+    diff = (means["incongruent"] - means["congruent"])
+    # expect a solid Stroop effect around ~100ms on this seed; allow wide but positive range
+    assert diff.mean() > 60 and diff.mean() < 140
+
+    # mixed model on log RT with random intercept for subject
+    df = trials[trials["correct"] & trials["rt_ms"].between(200, 2000)].copy()
+    df["log_rt"] = np.log(df["rt_ms"])
+    # Use categorical for condition
+    md = smf.mixedlm("log_rt ~ C(condition)", df, groups=df["subject"])
+    res = md.fit(reml=False)
+    coef = res.params.get("C(condition)[T.incongruent]")
+    pval = res.pvalues.get("C(condition)[T.incongruent]")
+    assert 0.12 < coef < 0.22
+    assert pval < 1e-6